Comprehensive Map of Molecules Implicated in Obesity

Diabetes and obesity pathophysiology as a teaching tool to emphasize physiology core concepts

Diabetes mellitus and obesity are major public health issues that significantly impact the health care system. The next generation of health care providers will need a deep understanding of the pathophysiology of these diseases if we are to prevent, treat, and eventually cure these diseases and ease the burden on patients and the health care system. Physiology core concepts are a set of core principles, or "big ideas," identified by physiology educators that are thought to promote long-term retention, create a deeper understanding, and help with formation of critical thinking skills. Here we describe our scaffolded teaching approach in an upper year undergraduate pathophysiology course to educate students about these two diseases and discuss how learning about the basis of these highly integrative diseases from the biochemical to whole body level is a meaningful tool in the physiology educator toolbox to reinforce physiology core concepts. This teaching strategy is designed to engage students in the scientific process and hone their problem-solving skills such that they are hopefully equipped to treat and eventually cure these diseases as they move forward in their careers.NEW & NOTEWORTHY Students often struggle with integration of physiological systems. Type 2 diabetes mellitus and obesity are two related diseases that are useful to explore the interdependence of physiological systems and multiple physiology core concepts. Deep learning about these diseases has the potential to dramatically improve the health care system of the future.

Body Composition and Metabolic Dysfunction Really Matter for the Achievement of Better Outcomes in High-Grade Serous Ovarian Cancer

Although obesity-associated metabolic disorders have a negative impact on various cancers, such evidence remains controversial for ovarian cancer. Here, we aimed to evaluate the impact of body composition (BC) and metabolism disorders on outcomes in high-grade serous ovarian cancer (HGSOC).

METHODS

We analyzed clinical/genomic data from two cohorts (PUC n = 123/TCGA-OV n = 415). BC was estimated using the measurement of adiposity/muscle mass by a CT scan. A list of 425 genes linked to obesity/lipid metabolism was used to cluster patients using non-negative matrix factorization. Differential expression, gene set enrichment analyses, and Ecotyper were performed. Survival curves and Cox-regression models were also built-up.

RESULTS

We identified four BC types and two clusters that, unlike BMI, effectively correlate with survival. High adiposity and sarcopenia were associated with worse outcomes. We also found that recovery of a normal BC and drug interventions to correct metabolism disorders had a positive impact on outcomes. Additionally, we showed that immune-cell-depleted microenvironments predominate in HGSOC, which was more evident among the BC types and the obesity/lipid metabolism cluster with worse prognosis.

CONCLUSIONS

We have demonstrated the relevance of BC and metabolism disorders as determinants of outcomes in HGSOC. We have shone a spotlight on the relevance of incorporating corrective measures addressing these disorders to obtain better results.

A Data-Driven Approach to Construct a Molecular Map of Trypanosoma cruzi to Identify Drugs and Vaccine Targets

Chagas disease (CD) is endemic in large parts of Central and South America, as well as in Texas and the southern regions of the United States. Successful parasites, such as the causative agent of CD, Trypanosoma cruzi have adapted to specific hosts during their phylogenesis. In this work, we have assembled an interactive network of the complex relations that occur between molecules within T. cruzi. An expert curation strategy was combined with a text-mining approach to screen 10,234 full-length research articles and over 200,000 abstracts relevant to T. cruzi. We obtained a scale-free network consisting of 1055 nodes and 874 edges, and composed of 838 proteins, 43 genes, 20 complexes, 9 RNAs, 36 simple molecules, 81 phenotypes, and 37 known pharmaceuticals. Further, we deployed an automated docking pipeline to conduct large-scale docking studies involving several thousand drugs and potential targets to identify network-based binding propensities. These experiments have revealed that the existing FDA-approved drugs benznidazole (Bz) and nifurtimox (Nf) show comparatively high binding energies to the T. cruzi network proteins (e.g., PIF1 helicase-like protein, trans-sialidase), when compared with control datasets consisting of proteins from other pathogens. We envisage this work to be of value to those interested in finding new vaccines for CD, as well as drugs against the T. cruzi parasite.

The Role of Molecular and Hormonal Factors in Obesity and the Effects of Physical Activity in Children

Obesity and overweight are defined as abnormal fat accumulations. Adipose tissue consists of more than merely adipocytes; each adipocyte is closely coupled with the extracellular matrix. Adipose tissue stores excess energy through expansion. Obesity is caused by the abnormal expansion of adipose tissue as a result of adipocyte hypertrophy and hyperplasia. The process of obesity is controlled by several molecules, such as integrins, kindlins, or matrix metalloproteinases. In children with obesity, metabolomics studies have provided insight into the existence of unique metabolic profiles. As a result of low-grade inflammation in the system, abnormalities were observed in several metabolites associated with lipid, carbohydrate, and amino acid pathways. In addition, obesity and related hormones, such as leptin, play an instrumental role in regulating food intake and contributing to childhood obesity. The World Health Organization states that physical activity benefits the heart, the body, and the mind. Several noncommunicable diseases, such as cardiovascular disease, cancer, and diabetes, can be prevented and managed through physical activity. In this work, we reviewed pediatric studies that examined the molecular and hormonal control of obesity and the influence of physical activity on children with obesity or overweight. The purpose of this review was to examine some orchestrators involved in this disease and how they are related to pediatric populations. A larger number of randomized clinical trials with larger sample sizes and long-term studies could lead to the discovery of new key molecules as well as the detection of significant factors in the coming years. In order to improve the health of the pediatric population, omics analyses and machine learning techniques can be combined in order to improve treatment decisions.

Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches

An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

Automated inference of Boolean models from molecular interaction maps using CaSQ

Motivation

Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner.

Results

We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability.

Availability and implementation

The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq.

Supplementary information

Supplementary data are available at Bioinformatics online.

Discovery and characterization of functional modules associated with body weight in broilers

Aim of the present study was to investigate whether body weight (BW) in broilers is associated with functional modular genes. To this end, first a GWAS for BW was conducted using 6,598 broilers and the high density SNP array. The next step was to search for positional candidate genes and QTLs within strong LD genomic regions around the significant SNPs. Using all positional candidate genes, a network was then constructed and community structure analysis was performed. Finally, functional enrichment analysis was applied to infer the functional relevance of modular genes. A total number of 645 positional candidate genes were identified in strong LD genomic regions around 11 genome-wide significant markers. 428 of the positional candidate genes were located within growth related QTLs. Community structure analysis detected 5 modules while functional enrichment analysis showed that 52 modular genes participated in developmental processes such as skeletal system development. An additional number of 14 modular genes (GABRG1, NGF, APOBEC2, STAT5B, STAT3, SMAD4, MED1, CACNB1, SLAIN2, LEMD2, ZC3H18, TMEM132D, FRYL and SGCB) were also identified as related to body weight. Taken together, current results suggested a total number of 66 genes as most plausible functional candidates for the trait examined.

Identification of Novel Candidate Markers of Type 2 Diabetes and Obesity in Russia by Exome Sequencing with a Limited Sample Size

Type 2 diabetes (T2D) and obesity are common chronic disorders with multifactorial etiology. In our study, we performed an exome sequencing analysis of 110 patients of Russian ethnicity together with a multi-perspective approach based on biologically meaningful filtering criteria to detect novel candidate variants and loci for T2D and obesity. We have identified several known single nucleotide polymorphisms (SNPs) as markers for obesity (rs11960429), T2D (rs9379084, rs1126930), and body mass index (BMI) (rs11553746, rs1956549 and rs7195386) (p < 0.05). We show that a method based on scoring of case-specific variants together with selection of protein-altering variants can allow for the interrogation of novel and known candidate markers of T2D and obesity in small samples. Using this method, we identified rs328 in LPL (p = 0.023), rs11863726 in HBQ1 (p = 8 × 10⁻⁵), rs112984085 in VAV3 (p = 4.8 × 10⁻⁴) for T2D and obesity, rs6271 in DBH (p = 0.043), rs62618693 in QSER1 (p = 0.021), rs61758785 in RAD51B (p = 1.7 × 10⁻⁴), rs34042554 in PCDHA1 (p = 1 × 10⁻⁴), and rs144183813 in PLEKHA5 (p = 1.7 × 10⁻⁴) for obesity; and rs9379084 in RREB1 (p = 0.042), rs2233984 in C6orf15 (p = 0.030), rs61737764 in ITGB6 (p = 0.035), rs17801742 in COL2A1 (p = 8.5 × 10⁻⁵), and rs685523 in ADAMTS13 (p = 1 × 10⁻⁶) for T2D as important susceptibility loci in Russian population. Our results demonstrate the effectiveness of whole exome sequencing (WES) technologies for searching for novel markers of multifactorial diseases in cohorts of limited size in poorly studied populations.

Computational Systems Biology Approach for the Study of Rheumatoid Arthritis: From a Molecular Map to a Dynamical Model

In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts' advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts' behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease. Leaning on the RA knowledgebase and using the web platform Cell Collective, we are currently building a Boolean large scale dynamical model for the study of RA fibroblasts' activation.

NFPscanner: a webtool for knowledge-based deciphering of biomedical networks

Background

Many biological pathways have been created to represent different types of knowledge, such as genetic interactions, metabolic reactions, and gene-regulating and physical-binding relationships. Biologists are using a wide range of omics data to elaborately construct various context-specific differential molecular networks. However, they cannot easily gain insight into unfamiliar gene networks with the tools that are currently available for pathways resource and network analysis. They would benefit from the development of a standardized tool to compare functions of multiple biological networks quantitatively and promptly.

Results

To address this challenge, we developed NFPscanner, a web server for deciphering gene networks with pathway associations. Adapted from a recently reported knowledge-based framework called network fingerprint, NFPscanner integrates the annotated pathways of 7 databases, 4 algorithms, and 2 graphical visualization modules into a webtool. It implements 3 types of network analysis:

Fingerprint: Deciphering gene networks and highlighting inherent pathway modules

Alignment: Discovering functional associations by finding optimized node mapping between 2 gene networks

Enrichment: Calculating and visualizing gene ontology (GO) and pathway enrichment for genes in networks

Users can upload gene networks to NFPscanner through the web interface and then interactively explore the networks’ functions.

Conclusions

NFPscanner is open-source software for non-commercial use, freely accessible at http://biotech.bmi.ac.cn/nfs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1673-1) contains supplementary material, which is available to authorized users.

Correlation of TLR4 and KLF7 in Inflammation Induced by Obesity

Objective Recent studies have revealed a link between toll-like receptors (TLRs), Kruppel-like factors (KLFs), and the adipose tissue inflammation associated with obesity. TLR4 is associated with chronic inflammation in obesity. KLF7 is known to play an important role in the differentiation of adipocytes, but its role in visceral adipose tissue inflammation has not yet been investigated. Thus, the objective of this study was to determine the correlation of TLR4 and KLF7 in inflammation induced by obesity. Methods A total of 32 Wistar male rat subjects were fed in the center for experimental animals of Shihezi University. The rats were divided into normal control (NC) and high-fat diet (HFD) group. Surgical instruments were used to collect rats' visceral adipose tissue samples in the 10th week after HFD feeding. Ninety-five Uygur subjects between 20 and 90 years old were enrolled in the present study. The subjects were divided into two groups: the normal control group (NC, 18.0 kg/m² ≤ BMI ≤ 23.9 kg/m², n = 50) and the obesity group (OB, BMI ≥ 28 kg/m², n = 45), and visceral adipose tissue was collected from the subjects. Anthropometric and clinical parameters were measured using standard procedures; biochemical indices were detected using the glucose oxidase-peroxidase method and a standardized automatic biochemistry analyzer; the plasma levels of inflammatory factors and adipocytokines were measured by enzyme-linked immunosorbent assay (ELISA); the mRNA and protein expression levels of key genes involved in the inflammatory signaling pathway were measured by real-time PCR and Western blot. Results In rats, compared with the NC group, the weight, Lee's index, waist circumference, visceral fat mass, and the plasma level of Glu, TG, FFA, and TNF-α were higher in the HFD group, while the plasma levels of LPT and APN were significantly lower in the HFD group in the 10th week. Furthermore, compared with the NC group, visceral adipose tissue's mRNA expression levels of TLR4, KLF7, and SRC were higher in the HFD group, and KLF7 was significantly positively correlated with LDL, TLR4, SRC, and IL-6 (P < 0.05). Meanwhile, in the Uygur population, the plasma levels of TG, LDL, and TNF-α in the OB group were significantly higher than those in the NC group (P < 0.05). Moreover, compared with the NC group, visceral adipose tissue's mRNA expression levels of TLR4, KLF7, and SRC were significantly higher in the OB group (P < 0.05), and KLF7 was significantly positively correlated with TC, TLR4, MYD88, SRC, and IL-6 (P < 0.05); the protein expression levels of TLR4 and KLF7 were significantly higher than those in the NC group (P < 0.05). Conclusion Higher expression of TLR4 and KLF7 may play a vital role in the process of inflammation induced by obesity in visceral adipose tissue.

A new method of finding groups of coexpressed genes and conditions of coexpression

Background

To study a biological phenomenon such as finding mechanism of disease, common methodology is to generate the microarray data in different relevant conditions and find groups of genes co-expressed across conditions from such data. These groups might enable us to find biological processes involved in a disease condition. However, more detailed understanding can be made when information of a biological process associated with a particular condition is obtained from the data. Many algorithms are available which finds groups of co-expressed genes and associated conditions of co-expression that can help finding processes associated with particular condition. However, these algorithms depend on different input parameters for generating groups. For real datasets, it is difficult to use these algorithms due to unknown values of these parameters.

Results

We present here an algorithm, clustered groups, which finds groups of co-expressed genes and conditions of co-expression with minimal input from user. We used random datasets to derive a cutoff on the basis of which we filtered the resultant groups and showed that this can improve the relevance of obtained groups. We showed that the proposed algorithm performs better than other known algorithms on both real and synthetic datasets. We have also shown its application on a temporal microarray dataset by extracting biclusters and biological information hidden in those biclusters.

Conclusions

Clustered groups is an algorithm which finds groups of co-expressed genes and conditions of co-expression using only a single parameter. We have shown that it works better than other existing algorithms. It can be used to find these groups in different data types such as microarray, proteomics, metabolomics etc.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1356-3) contains supplementary material, which is available to authorized users.

Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram

Causal loop diagrams developed by groups capture a shared understanding of complex problems and provide a visual tool to guide interventions. This paper explores the application of network analytic methods as a new way to gain quantitative insight into the structure of an obesity causal loop diagram to inform intervention design. Identification of the structural features of causal loop diagrams is likely to provide new insights into the emergent properties of complex systems and analysing central drivers has the potential to identify leverage points. The results found the structure of the obesity causal loop diagram to resemble commonly observed empirical networks known for efficient spread of information. Known drivers of obesity were found to be the most central variables along with others unique to obesity prevention in the community. While causal loop diagrams are often specific to single communities, the analytic methods provide means to contrast and compare multiple causal loop diagrams for complex problems.

Correlation of A2bAR and KLF4/KLF15 with Obesity-Dyslipidemia Induced Inflammation in Uygur Population

In this paper, the researchers collected visceral adipose tissue from the Uygur population, which were divided into two groups: the normal control group (NC, n = 50, 18.0 kg/m(2) ≤ BMI ≤ 23.9 kg/m(2)) and the obese group (OB, n = 45, BMI ≥ 28 kg/m(2)), and then use real-time PCR to detect the mRNA expression level of key genes involved in inflammation signaling pathway. The findings suggest that, in obese status, the lower expression level of A2bAR, KLF4, and KLF15 of visceral adipose tissue may correlate with obese-dyslipidemia induced inflammation in Uygur population.